Leveraging Machine Learning for Spreading Factor Optimization in Lora WAN Networks

Farhan Nisar; Mahad Shah; Muhammad Nouman; Shahzada Fahim Jan; Muhammad Nauman Khan; Robina Safi; Arshad Farhad; Muhammad Touseef Irshad; Sana Shafiq

Authors

Farhan Nisar Department of Physical and Numerical Sciences, Qurtaba University, Peshawar
Mahad Shah Department of Physical and Numerical Sciences, Qurtaba University, Peshawar
Muhammad Nouman Department of Computer Systems Engineering, UET Peshawar
Shahzada Fahim Jan Department of Computer Systems Engineering, UET Peshawar
Muhammad Nauman Khan Department of Computer Science & IT, Agriculture University, Peshawar
Robina Safi Department of Computer Science, Abasyn University, Peshawar
Arshad Farhad Department of Computer Science, Bahria University, E-8 Islamabad
Muhammad Touseef Irshad Department of Computer Science, National University of Modern Languages, Peshawar
Sana Shafiq Department of Physical and Numerical Sciences, Qurtaba University, Peshawar

Keywords:

Internet of Things (IoT), Machine Learning (ML), LSTM, Spreading Factor (SF), Transmission Power (TP)

Abstract

The Internet of Things (IoT) has witnessed exponential growth and widespread integration across diverse sectors such as agriculture, logistics, smart cities, and healthcare. Among various IoT communication paradigms, the Long-Range Wide Area Network has emerged as a prominent and preferred technology, attributed to its extended transmission range, energy efficiency, and cost-effectiveness. Nevertheless, the escalating proliferation of IoT endpoints has amplified the complexity of efficient resource orchestration, particularly in Spreading Factor (SF) optimization within infrastructures. To mitigate this challenge, this study introduces a Machine Learning–driven Adaptive Data Rate (ML-ADR) framework for dynamic SF management. A Long Short-Term Memory (LSTM) neural network was meticulously trained using a dataset synthesized via ns-3 network simulations to achieve optimal SF classification. The pre-trained LSTM model was subsequently deployed on end-device nodes to enable intelligent and adaptive SF allocation using real-time data during simulation. Experimental evaluations reveal significant enhancements in packet delivery ratio and notable reductions in energy consumption, thereby validating the efficacy and scalability of the proposed ML-ADR approach.

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